In order to reproduce a color original, it is necessary to determine, for each pixel of the original, the components of the pixel color and to thereafter determine the amounts of colorants required to reproduce the pixel color. The most common way of determining these amounts is through the use of a scanning densitometer. A densitometer scans a color transparency pixel by pixel and develops a signal triplet representing the approximate densities of the red, green and blue color components forming the color of each pixel. However, scanning densitometers have inherent limitations which limit the usefulness thereof. The most noticeable limitation is due to the fact that the density spectra of the transparency colorants overlap each other resulting in unwanted densities and crosstalk between densitometer color channels. This crosstalk is unavoidable and must be compensated for to achieve true color reproduction. If the filter bandwidths are very narrow, a 3.times.3 matrix can remove the crosstalk to obtain true colorant values. Unfortunately, the filters used in ordinary densitometers are not ideal inasmuch as each has a bandwidth which is insufficiently narrow and as a result nonlinearities arise in the relationship between densitometer outputs and colorant values. This nonlinear relationship results in the inability of the matrix to precisely determine the amounts of colorants forming a pixel color from the densitometer outputs for all colorant combinations. Hence, the resultant colorant values do not permit completely satisfactory color reproduction.
A different method of correcting densitometer output signals to eliminate unwanted densities involves the use of a lookup table (LUT) stored with empirically determined data. For example, Clark, et al., U.S. Pat. Nos. 4,477,833 and 4,481,532, owned by the assignee of the instant application, disclose color reproduction systems utilizing a method of storing empirically derived values in a lookup table and a method of interpolating between such stored values. The lookup table is addressed by scanned RGB density values to obtain cyan, magenta and yellow (CMY) density values suitable for an output device.
While color reproduction using a densitometer has been found to be advantageous in that the measurements relate a color to the amount of colorants required to reproduce the color and while years of experience have been gained through the use of densitometers, it has been found that the ability to soft proof (i.e., reproduce the color on different media, such as a CRT) is limited and modification and correction must be done empirically.
The use of a colorimeter (i.e., a device which develops a triplet of numbers X, Y, Z formed by integrating over a spectral response that matches the response defined by the CIE Commission of 1931 for the two degree or the ten degree observer) to obtain values in a colorimetric space allows color correction and soft proofing to be accomplished in an easier fashion. Also, many image processing, manipulation, gamut translation and achromatic processing tasks are more readily accomplished. Further, the colorimetric color space is related to the average human observer, and hence colors are defined in a visually precise manner. However, not as much experience has been gained in the practicality and use of a colorimeter as compared with a densitomer.
The traditional way to scan colorimetrically involves replacing the spectral response elements including the red, green and blue filters of the densitometric scanner with spectral response elements including filters that closely approximate the broadband colorimetric response curves or some linear combination of such curves. The resulting data developed by such a scanner is colorimetric in nature, i.e., the signals define a color in a colorimetric space. However, in order to accomplish this, a greatly-modified scanner must be built, which is potentially a difficult task and costly.
A further color reproduction system is disclosed in Schreiber, U.S. Pat. No. 4,500,919. A scanner is used to scan a color original on a pixel-by-pixel basis and tristimulus appearance values are developed representing the colors of the pixels. If the scanner is not equipped with true color matching function filters (i.e., colorimetric filters) then a converter is required to convert the scanner outputs into tristimulus appearance signals. In this case, if the deviation from the correct filter characteristics is small, then the conversion is done by means of a 3.times.3 linear matrix. On the other hand, if the deviation from the correct filter characteristics is very large, a lookup table is used to convert the scanner outputs to tristimulus appearance values.
Schreiber also discloses methods of loading a lookup table. However, the Schreiber methods, like all other methods that load lookup tables using empirically determined values, must derive all entries for the table experimentally and substantially simultaneously. Also, according to one of the methods disclosed by Schreiber, all of the entries in the table must be manipulated in an iterative process as a set. Further, the Schreiber methods begin with a first rough guess at table entries and then adjust table entries to successively correct same.